class_docstr: an unassuming Transformer block
code: def __init__(self, config: GPTConfig) -> None:
      super().__init__()
      self.ln1 = nn.LayerNorm(config.n_embd)
      self.ln2 = nn.LayerNorm(config.n_embd)
      self.attn = MultiheadAttentionLayer(config)
      self.mlp = nn.Sequential(
          nn.Linear(config.n_embd, 4 * config.n_embd),
          nn.GELU(),
          nn.Linear(4 * config.n_embd, config.n_embd),
          nn.Dropout(config.resid_pdrop),
      )
  
imports: import math
  from dataclasses import dataclass
  from typing import Optional, Tuple
  import torch
  import torch.nn
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new Block object from inherited base classes: nn with example: obj = Block(config)